Like vanilla GANs, the implementation of cGANs is straightforward. Keras provides enough flexibility to code complex generative adversarial networks. In this section, we will implement the generator network, the discriminator network, and the encoder network used in cGANs. Let's start by implementing the encoder network.

Before starting to write the implementations, create a Python file called main.py and import the essential modules, as follows:

import math
import os
import time
from datetime import datetime

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from keras import Input, Model
from keras.applications import InceptionResNetV2
from keras.callbacks import TensorBoard
from keras.layers import Conv2D, Flatten, Dense, BatchNormalization, Reshape, concatenate, LeakyReLU, Lambda, 
    K, Conv2DTranspose, Activation, UpSampling2D, Dropout
from keras.optimizers import Adam
from keras.utils import to_categorical
from keras_preprocessing import image
from scipy.io import loadmat